Vehicle battery health diagnosis through electrode potential estimation

Abstract

Vehicle battery health diagnostics are made through electrode potential estimation. A system and method for diagnosing vehicle battery health using electrode potential estimates is presented. A first set of anode electrode potentials and a first set of cathode electrode potentials of an original vehicle battery cell are determined at a first charge rate. A second set of anode electrode potentials and a second set of cathode electrode potentials of the original vehicle battery cell are determined at a second charge rate. A determination of a set of optimized health indicators is made based on the first set of anode electrode potentials, the first set of cathode electrode potentials, the second set of anode electrode potentials, and the second set of cathode electrode potentials. After the first number of cycles, a test of the vehicle battery is performed based on the optimized health indicator to determine faulty one or more battery cells of the vehicle battery.

Description

[0001] Introduction Background Art

[0002] Vehicles are rapidly integrating an increasing number of technological components into their systems, especially in the direction of hybrid-electric and battery-based electric vehicles (EVs). EVs are becoming increasingly popular as an environmentally friendly alternative to traditional gasoline-powered vehicles. However, one of the challenges associated with EVs is the need for a reliable and healthy battery system.

[0003] The ability to perform early diagnosis and resolve battery cell defects within a vehicle battery system can avoid future performance and reliability issues. Early battery testing and diagnosis at the manufacturing plant and / or during the early stages of operation can allow for remedial actions to avoid future problems. Additionally, early diagnosis can inform control and manufacturing improvements. Summary of the Invention

[0004] Disclosed herein is a system and method for diagnosing the health of a vehicle battery using electrode potential estimation. By testing a pristine battery cell (e.g., a known good battery cell) in a laboratory, for example, electrode potential (EP) curves can be generated at different charge rates (e.g., C-rates). A pristine can be defined as a battery cell having defects below a predetermined threshold, or in other words, a battery cell having performance above a predetermined threshold. The charge and discharge rates of a battery can be governed by the C-rate. For example, a battery rated at 1C capacity may mean that a fully charged battery rated at 1 Ah should provide 1 A for one hour. The same battery discharged at 0.5C or C / 2 can provide 500 mA for two hours, and at 2C can deliver 2 A for 30 minutes. In a similar manner, a C / 100 rate can indicate a low charge / discharge rate with a 100-hour capacity, as opposed to a C / 3 rate, which can indicate a typical vehicle battery charge / discharge rate with a three-hour capacity.

[0005] EP curves for different C-rates can be experimentally derived for a pristine battery cell to generate an optimal set of EP curves for possible cycling scenarios. This set of EP curves can then be compared to the EP curve for a vehicle battery to diagnose differences within the vehicle battery, which can be further diagnosed and corrected.

[0006] Accordingly, the present disclosure will present new methods for estimating anode / cathode potential, where the anode / cathode potential can be estimated by using EP curves for a pristine battery cell at different C-rates and then by using constant current (CC) charge or discharge data. Additionally, as will be discussed, instead of using CC charge data, estimated open circuit voltage value data can also be used to estimate the anode / cathode potential.

[0007] Accordingly, a method for diagnosing the health of a vehicle battery using electrode potential estimation may include, for an original vehicle battery cell, experimentally determining a first set of anode electrode potentials and a first set of cathode electrode potentials of the original vehicle battery cell at a first charging rate (C-rate). The diagnosis may continue by: for a vehicle battery cell, experimentally determining a second set of anode electrode potentials and a second set of cathode electrode potentials of the original vehicle battery cell at a second charging rate (C-rate). Additionally, the diagnosis may include determining a set of optimized health metrics based on the first set of anode electrode potentials and the first set of cathode electrode potentials and the second set of anode electrode potentials and the second set of cathode electrode potentials, and then applying the optimized health metrics to the vehicle battery after a first number of cycles. Based on the application of the optimized health metrics, a determination of the presence of one or more faulty battery cells of the vehicle battery may be made.

[0008] Another aspect of the method may include that the original vehicle battery cell is of the lithium-ion type.

[0009] Another aspect of the method may include that the second charging rate (C-rate) is greater than C / 100, where C represents the capacity of the vehicle battery measured in ampere-hours.

[0010] Another aspect of the method may further include identifying an appropriate range of the health metrics.

[0011] Another aspect of the method may include that determining the set of optimized health metrics includes measuring a plurality of cell voltages and a plurality of current values during constant current charging or discharging of the original vehicle battery cell.

[0012] Another aspect of the method may include that determining the set of optimized health metrics is based on solving an optimization problem that minimizes the health metrics, where where Q k = I*k*Δt is the amount of charge or discharge of the cell up to time k*Δt; C N is the nominal capacity of the cell; I is the current; and Δt is the sampling period. and is the inverse function of q ca and q an as described below.

[0013] Another aspect of the method may include that determining the set of optimized health metrics is based on:

[0014]

[0015]

[0016] where a, b, c, d, e, and f represent health metric values; CN is the nominal capacity of the battery cell; and is the function of charge with respect to the anode potential at the and C-rate; and is the function of charge with respect to the cathode potential at the and C-rate. and as well as and can be determined from experimental measurements.

[0017] Another aspect of the method can include determining that the set of optimized health metrics includes measuring a plurality of battery cell open circuit voltages (OCV) and a plurality of current values at a constant discharge rate of the original vehicle battery cells.

[0018] Another aspect of the method can include determining that the set of optimized health metrics is based on solving an optimization problem:

[0019]

[0020] where Q k = Q k-1 + I k *Δt;

[0021] Q0 = SOC0 * C N where C N represents the nominal capacity of the original vehicle battery cells.

[0022] Another aspect of the present disclosure can include a system for diagnosing the health of a vehicle battery using electrode potential estimation, the system including a computer to determine a first set of anode electrode potentials and a first set of cathode electrode potentials of the vehicle battery cells at a first charging rate for the original vehicle battery cells. The computer can also determine a second set of anode electrode potentials and a second set of cathode electrode potentials of the original vehicle battery cells at a second charging rate for the original vehicle battery cells. Additionally, the computer can determine a set of optimized health metrics based on the first set of anode electrode potentials and the first set of cathode electrode potentials as well as the second set of anode electrode potentials and the second set of cathode electrode potentials. Additionally, a battery test system can be used to test the vehicle battery using the optimized health metrics after a first number of cycles and determine one or more faulty battery cells of the vehicle battery based on the applied optimized health metrics.

[0023] Another aspect of the present disclosure can include that the original vehicle battery cells are of the lithium-ion type.

[0024] Another aspect of the present disclosure may include that a second charging rate (C-rate) is greater than C / 100, where C represents the capacity of the vehicle battery measured in ampere-hours.

[0025] Another aspect of the present disclosure may include that the computer can identify an appropriate range of health metrics.

[0026] Another aspect of the present disclosure may include that the computer can determine that the set of optimized health metrics may include measuring a plurality of cell voltages and a plurality of current values under constant current charging or discharging of the original vehicle battery cell.

[0027] Another aspect of the present disclosure may include that the computer can determine the set of optimized health metrics based on solving the following optimization problem:

[0028]

[0029] where Q k = I * k * Δt, and Δt is the sampling period.

[0030] Another aspect of the present disclosure may include that the computer can determine the set of optimized health metrics based on the following

[0031]

[0032]

[0033] where a, b, c, d, e, and f represent health metric values.

[0034] Another aspect of the present disclosure may include that the computer can determine that the set of optimized health metrics may include measuring a plurality of cell open circuit voltages (OCVs) and a plurality of current values under a constant discharge rate of the original vehicle battery cell.

[0035] Another aspect of the present disclosure may include that the computer, when determining the set of optimized health metrics, is based on solving an optimization problem:

[0036] where Q k = Q k-1 + I k * Δt;

[0037] Q0 = SOC0 * C N where C N represents the nominal capacity of the original vehicle battery cell.

[0038] Another aspect of the present disclosure may include a method for diagnosing the health of a vehicle battery using electrode potential estimation, the method including, for an original lithium-ion vehicle battery cell, determining a first set of anode electrode potentials and a first set of cathode electrode potentials of the original lithium-ion vehicle battery cell at a first charging rate. The method may further include, for the original lithium-ion vehicle battery cell, determining a second set of anode electrode potentials and a second set of cathode electrode potentials of the original lithium-ion vehicle battery cell at a second charging rate, where the second charging rate (C-rate) is greater than C / 100, and where C represents the capacity of the vehicle battery measured in ampere-hours. Additionally, determining a set of optimized health metrics may be based on the first set of anode electrode potentials and the first set of cathode electrode potentials and the second set of anode electrode potentials and the second set of cathode electrode potentials. Identifying an appropriate range of the health metrics may be by: applying the optimized health metrics to the vehicle battery after a first number of cycles, and determining one or more faulty battery cells of the vehicle battery based on the applied optimized health metrics.

[0039] Another aspect of the method may include, where determining the set of optimized health metrics includes measuring a plurality of cell voltages and a plurality of current values during constant current charging or discharging of the original vehicle battery cell.

[0040] The present invention also includes the following solutions:

[0041] Solution 1. A method for diagnosing the health of a vehicle battery using electrode potential estimation, including:

[0042] For an original vehicle battery cell, determining a first set of anode electrode potentials and a first set of cathode electrode potentials of the original vehicle battery cell at a first charging rate (first C-rate);

[0043] For an original vehicle battery cell, determining a second set of anode electrode potentials and a second set of cathode electrode potentials of the original vehicle battery cell at a second charging rate (second C-rate);

[0044] Determining a set of optimized health metrics based on the first set of anode electrode potentials and the first set of cathode electrode potentials and the second set of anode electrode potentials and the second set of cathode electrode potentials;

[0045] Testing the vehicle battery based on the optimized health metrics after a first number of cycles; and

[0046] Determining one or more faulty battery cells of the vehicle battery based on the test of the vehicle battery.

[0047] Solution 2. The method according to Solution 1, where the original vehicle battery cell includes a lithium-ion type.

[0048] Solution 3. The method according to Solution 1, wherein the second charging rate (second C-rate) is greater than C / 100, where C represents the capacity of the vehicle battery measured in ampere-hours.

[0049] Solution 4. The method according to Solution 1, further comprising identifying an appropriate range of the health metric.

[0050] Solution 5. The method according to Solution 1, wherein determining the set of optimized health metrics includes measuring a plurality of cell voltages and a plurality of current values under constant current charging or discharging of the original vehicle battery cell.

[0051] Solution 6. The method according to Solution 5, wherein determining the set of optimized health metrics is based on solving an optimization problem, the optimization problem comprising:

[0052] where Q k = I * k * Δt, and Δt is the sampling period.

[0053] Solution 7. The method according to Solution 5, wherein determining the set of optimized health metrics is based on:

[0054]

[0055]

[0056] where a, b, c, d, e, and f represent health metric values.

[0057] Solution 8. The method according to Solution 1, wherein determining the set of optimized health metrics includes measuring a plurality of cell open circuit voltages (OCV) and a plurality of current values at a constant discharge rate of the original vehicle battery cell.

[0058] Solution 9. The method according to Solution 7, wherein determining the set of optimized health metrics is based on solving an optimization problem, the optimization problem comprising:

[0059] where Q k = Q k-1 + I k * Δt;

[0060] Q0 = SOC0 * C N where C N represents the nominal capacity of the original vehicle battery cell.

[0061] Solution 10. A system for diagnosing the health of a vehicle battery using electrode potential estimation, comprising:

[0062] A computer configured to determine, for an original vehicle battery cell, a first set of anode electrode potentials and a first set of cathode electrode potentials of the original vehicle battery cell at a first charging rate (first C-rate);

[0063] The computer configured to determine, for the original vehicle battery cell, a second set of anode electrode potentials and a second set of cathode electrode potentials of the original vehicle battery cell at a second charging rate (second C-rate);

[0064] The computer configured to determine a set of optimized health metrics based on the first set of anode electrode potentials and the first set of cathode electrode potentials and the second set of anode electrode potentials and the second set of cathode electrode potentials;

[0065] A battery test system configured to test a vehicle battery after a first number of cycles based on the optimized health metrics; and

[0066] The battery test system configured to determine one or more faulty battery cells of the vehicle battery based on the test of the vehicle battery.

[0067] Claim 11. The system according to claim 10, wherein the original vehicle battery cell comprises a lithium-ion type.

[0068] Claim 12. The system according to claim 10, wherein the second charging rate (second C-rate) is greater than C / 100, where C represents the capacity of the vehicle battery measured in ampere-hours.

[0069] Claim 13. The system according to claim 10, further comprising the computer configured to identify an appropriate range of the health metrics.

[0070] Claim 14. The system according to claim 10, further comprising the computer configured to determine that the set of optimized health metrics comprises measuring a plurality of cell voltages and a plurality of current values during constant current charging of the original vehicle battery cell.

[0071] Claim 15. The system according to claim 14, further comprising the computer configured to determine the set of optimized health metrics based on solving an optimization problem, the optimization problem comprising:

[0072] where Q k = I * k * Δt, and Δt is a sampling period.

[0073] Claim 16. The system according to claim 14, further comprising the computer configured to determine the set of optimized health metrics based on:

[0074]

[0075]

[0076] Where a, b, c, d, e, and f represent health indicator values.

[0077] Solution 17. The system according to Solution 10 further includes the computer configured to determine that the set of optimized health indicators includes measuring a plurality of battery cell open circuit voltages (OCV) and a plurality of current values at a constant discharge rate of the original vehicle battery cell.

[0078] Solution 18. The system according to Solution 16, wherein determining the set of optimized health indicators is based on solving an optimization problem, and the optimization problem includes:

[0079] Where Q k = Q k-1 + I k *Δt;

[0080] Q0 = SOC0 * C N where C N represents the nominal capacity of the original vehicle battery cell.

[0081] Solution 19. A method for diagnosing the health of a vehicle battery using electrode potential estimation, including:

[0082] For an original lithium-ion vehicle battery cell, determining a first set of anode electrode potentials and a first set of cathode electrode potentials of the original lithium-ion vehicle battery cell at a first charging rate (first C-rate);

[0083] For an original lithium-ion vehicle battery cell, determining a second set of anode electrode potentials and a second set of cathode electrode potentials of the original lithium-ion vehicle battery cell at a second charging rate, where the second charging rate (second C-rate) is greater than C / 100, and C represents the capacity of the vehicle battery measured in ampere-hours;

[0084] Determining a set of optimized health indicators based on the first set of anode electrode potentials and the first set of cathode electrode potentials and the second set of anode electrode potentials and the second set of cathode electrode potentials;

[0085] Identifying an appropriate range of the health indicators;

[0086] After a first number of cycles, testing the vehicle battery based on the optimized health indicators; and

[0087] Determine one or more faulty battery cells of the vehicle battery based on testing of the vehicle battery.

[0088] Solution 20. The method according to Solution 19, wherein determining the set of optimized health metrics includes measuring a plurality of cell voltages and a plurality of current values under constant current charging or discharging of the original lithium-ion vehicle battery cells.

[0089] When understood in conjunction with the accompanying drawings and the appended claims, the above and other features and attendant advantages of the present disclosure will become readily apparent from the following detailed description of illustrative examples and modes for practicing the present disclosure. In addition, the present disclosure expressly includes combinations and sub-combinations of the elements and features presented above and below. Description of the Drawings

[0090] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0091] Figure 1 is an illustration of a process for diagnosing vehicle battery health using electrode potential estimation in accordance with the present disclosure.

[0092] Figure 2 is an illustration of a vehicle battery cell and associated characteristics in accordance with the present disclosure.

[0093] Figure 3A 、 Figure 3B and Figure 3C illustrate electrode potential (EP) curves representing various charging rates in accordance with the present disclosure.

[0094] Figure 4 is an illustration of the optimization of cell health metrics in accordance with the present disclosure.

[0095] Figure 5A 、 Figure 5B 、 Figure 5C and Figure 5D illustrate the detection of a damaged battery cell from separator health in accordance with the present disclosure.

[0096] Figure 6 is a method for diagnosing vehicle battery health using electrode potential estimation in accordance with the present disclosure.

[0097] The accompanying drawings are not necessarily to scale and may present a degree of simplification of various preferred features of the present disclosure as disclosed herein (including, for example, specific dimensions, orientations, positions, and shapes). The details associated with such features will be determined in part by the particular intended application and use environment. Detailed Description

[0098] The present disclosure admits of embodiments in many different forms. Exemplary representations of the present disclosure are shown in the drawings and are described herein in detail as non-limiting examples of the disclosed principles. For this purpose, elements and limitations described in the abstract, introduction, summary of the invention, and detailed description sections but not explicitly set forth in the claims should not be incorporated into the claims by implication, inference, or otherwise, either individually or collectively.

[0099] For the purposes of this description, unless specifically disclaimed, the use of the singular includes the plural and vice versa, the terms “and” and “or” shall be both conjunctive and disjunctive, and the words “comprising,” “containing,” “including,” “having,” and the like shall mean “including but not limited to.” Additionally, approximating words such as “about,” “almost,” “substantially,” “generally,” “approximately,” etc. may be used herein in the sense of “being at, close to, or almost at” or “within 0-5% of” or “within acceptable manufacturing tolerances” or a logical combination thereof. As used herein, a component “configured to” perform a specified function is capable of performing the specified function without alteration, rather than merely having the potential to perform the specified function after further modification. In other words, the described hardware is specifically selected, created, implemented, utilized, programmed, and / or designed for the purpose of performing the specified function when it is expressly configured to perform the specified function.

[0100] Referring to the drawings, the leftmost digit of a reference numeral identifies the drawing in which that reference numeral first appears (e.g., the reference numeral “310” indicates that the element so numbered is first labeled or first appears in FIG. 3). Additionally, elements having the same reference numeral followed by a different alphabetic letter or other distinct marker (e.g., an apostrophe) indicate elements that may be identical in structure, operation, or form but may be identified as occurring at different positions in space or at different points in time (e.g., the reference numerals “110a” and “110b” may indicate two different input devices that may be functionally identical but may be located at different points in an analog field).

[0101] Figure 1 Process 100 for diagnosing the health of a vehicle battery using electrode potential estimation in accordance with an embodiment of the present disclosure is illustrated. Electric vehicle 110 may be an electric vehicle or a hybrid model or another design. Electric vehicle 110 may include a vehicle battery that includes a plurality of battery cells. However, the vehicle battery may include battery cells that may be damaged or defective. Damaged or defective vehicle battery cells may degrade their ability to hold a charge or may cause additional complications. Therefore, it is desirable to be able to detect, diagnose, and resolve vehicle battery problems.

[0102] An electric vehicle 110 can generate multiple data points for its vehicle battery unit at a particular time, such as voltage, charge capacity, current draw, temperature, etc. Such data points combined with the original battery unit parameters 120 can be used to estimate the anode and cathode potentials at 125 and generate a set of health metrics, as will be discussed in more detail. The original battery unit parameters 120 can be derived in an experimental or laboratory environment and represent ideal or perfect vehicle battery parameters.

[0103] At 130, the estimated anode and cathode potentials or electrode potential (EP) curves can be compared with the actual EP curve of a particular vehicle battery unit to diagnose and estimate the battery unit health. Based on such an analysis, at 140, a control process or a modification of a manufacturing process can be implemented to improve vehicle battery diagnosis and potential repair.

[0104] Figure 2 FIG. 7 is an illustration of a vehicle battery unit 200 and associated characteristics in accordance with an embodiment of the present disclosure. The vehicle battery unit 200 can include a cathode 210, a separator 220, and an anode 230. The vehicle battery unit 200 illustrates the flow of lithium ions, as shown here in a lithium-ion battery unit, where lithium ions can flow from the cathode 210 through the separator 220 to the anode 230 during charging and flow in the opposite direction during a discharge cycle. The separator 220 can be a thin porous membrane that physically separates the anode and cathode to prevent physical contact between the anode 230 and the cathode 210 while facilitating ion transport.

[0105] As shown in 240, the electrode potential EP of the anode an and the electrode potential EP of the cathode ca can be determined by the electrode chemical composition of the battery unit. Further, x / y can represent the normalized lithium surface density at the anode / cathode. EP ca and EP an The difference between can also be referred to as the open circuit voltage OCV of the battery unit, i.e., OCV = EP ca - EP an . Further, OCV can be a function of the battery unit charge (and is referred to as Q).

[0106] In addition, as will be shown in FIG. 3, according to an embodiment of the present disclosure, the electrode potentials at the cathode and anode can be plotted as their potential versus the state of charge (SOC) of the battery cell, which can be referred to as the EP curve. As will be shown, the EP curve discloses the non-linear result from the battery cell charge Q to the normalized lithium surface density x / y at the anode / cathode, except at low charging rates (e.g., c-rate of C / 100). However, the electric vehicle charging rate typically may occur at higher charging rates (e.g., C / 3 - C / 5). As depicted in 250, the equations for the EP curve, where the EP of the anode is a function of the charge capacity Q and is denoted as x, and where the EP of the cathode is also a function of the charge capacity Q and is denoted as y.

[0107] Figure 3A and Figure 3B respectively represent the EP curve 300A and the EP curve 300B of the original battery cell at different charging rates. For example, the EP curve 300A is an example of the EP curve at a charging rate of C / 100. The EP curve 300B is an example of the EP curve at a charging rate of C / 5. Given the EP curve 300A and the EP curve 300B at different charging rates, a set of results of the EP curve of the vehicle battery at a specific age (e.g., the number of charge / discharge cycles) can be determined. Such results are as shown by the EP curve 300C in Figure 3C .

[0108] The EP curve 300C can be determined by using the curves of the anode and cathode EP versus the state of charge (SOC) at two different C-rates as inputs. In the examples of the EP curve 300A and the EP curve 300B, those rates are 5 and 100. The curves can also be expressed as: For the original battery cell, where in this example j = 5 and k = 100. C can be described as the capacity of the battery cell; EP an is the electrode potential at the anode; EP ca is the electrode potential at the cathode, and g an is a function from EP an to soc at the anode; and g ca is a function from EP ca to soc at the cathode. Under constant current charging or discharging, there may also be a nominal capacity C low ,V high} and between {V high ,V low} for the time series data {V k ,I k} of the battery cell, where V can represent voltage and I can represent current. N .

[0109] The EP curve 300C can use the function Q / C with characterization parameters that can be used as health indicators N = q an (EP an ) and Q / C N = q ca (EP ca ) to represent. This can be expressed as follows, where the health indicator is shown as θ = {a, b, c, d, e, f}:

[0110]

[0111]

[0112] The health indicator θ = {a, b, c, d, e, f} can be estimated from the voltage and current of the vehicle battery cell. This can be achieved in two ways. The first method can be to use the battery cell voltage and current values {V k , I} during constant current (CC) charging or discharging. The second method can be to use the estimated open circuit voltage OCV k and the associated current I k .

[0113] Using a constant current during charging generates the EP curve using the following expression, as illustrated, for example, in the EP curve 300C:

[0114]

[0115] where Q k = I * k * Δt, and Δt is the sampling period.

[0116] The above equation is further supported by the following reasoning: During constant current charging or discharging:

[0117] V = OCV + I * R;

[0118] Note: OCV = EP ca - EP an

[0119] By minimizing θ = {a, b, c, d, e, f} in the above equation, the EP curve 300C can be generated, where in this example a = 0.78, b = 0.47, c = -0.07, d = 0.37, e = 0.8, and f = -0.21. According to an embodiment of the present disclosure, the solution can also be as Figure 4 shown, where the vertical axis shows dV / dQ or dV / dSOC, and the horizontal axis represents the state of charge (SOC) of the battery cell.

[0120] As mentioned above, the health index θ = {a, b, c, d, e, f} can also be estimated by using the estimated open circuit voltage OCV during discharging of the battery cell k and the associated current I k For discharging, the health index can be determined based on the estimated OCV k and the associated current I k as follows:

[0121] where Qk = Qk-1 + Ik*Δt;

[0122] Q0 = SOC0*C N , where C N represents the nominal capacity of the original vehicle battery cell.

[0123] k , Q k}. Fourth, solve the optimization to derive θ, for example,

[0124]

[0125] k , I k}. Fourth, solve the optimization to derive θ, for example,

[0126] where Q k = Q k-1 + I k *Δt; Q0 = 0. Figure 5A Figure 5B [[ID=4 in]] Figure 5C and Figure 5D Figure 5 is an example of the health index θ = {a, b, c, d, e, f} of a vehicle battery with twelve battery cells according to an embodiment of the present disclosure. The twelve battery cells are at various charging and discharging levels, i.e., cycles. As previously discussed, if the separator between the anode and the cathode becomes damaged, the battery may show a decline or complete failure. Figure 5A 、 Figure 5B 、 Figure 5C and Figure 5DIndicates the health metrics of a vehicle battery after 2, 50, 100, and 200 cycles, where the triangular icon represents a battery cell without induced faults, the square icon represents a battery cell with three holes cut in the separator, and the circular icon represents a battery cell with a slice introduced in the battery cell separator.

[0127] Figure 5A Represents the vehicle battery after only two cycles (e.g., during or immediately after manufacturing). However, the identified cluster 510 illustrates that the battery cells 9, 10, 11, and 12 with sliced separators do not perform as well as the other battery cells. Additionally, the two battery cells with three holes also do not perform at peak levels. At this point, other actions can be taken, such as providing the following feedback to the manufacturer: additional quality monitoring may be desirable. Figure 5B Represents the vehicle battery after 50 cycles. As shown by the cluster 520, the sliced battery cells among the previously identified battery cells 9, 10, 11, and 12 continue to degrade.

[0128] Figure 5C Represents the vehicle battery after 100 cycles and again indicates the severely damaged battery cells 9, 10, 11, and 12 in the cluster 530. Figure 5D Again illustrates the continued degradation of the battery cells 9, 10, 11, and 12 in the cluster 540.

[0129] Figure 6 Is a flowchart 600 according to an embodiment of the present disclosure, which illustrates a method for diagnosing the health of a vehicle battery using electrode potential estimation.

[0130] The flowchart 600 can start at step 605, which defines a method for diagnosing the health of a vehicle battery using electrode potential estimation. At step 610, the method can perform measurements of a first set of anode electrode potentials and a first set of cathode electrode potentials of the original vehicle battery cells at a first charging rate for the original vehicle battery cells, e.g., an EP curve. As discussed, regarding Figure 3A , the first set of anode electrode potentials and the first set of cathode electrode potentials of the original vehicle battery cells at the first charging rate can be represented by the EP curve 300A, which can be derived experimentally for the original battery cells. In this example, the EP curve 300A represents the curve of the battery cells at a charging rate of C / 100.

[0131] At step 615, the method can perform measurements on a second set of anode electrode potentials and a second set of cathode electrode potentials of the original vehicle battery cell at a second charging rate, which can be represented by EP curve 300B, and the EP curve 300B can be derived through experiments on the original battery cell. In this example, the EP curve 300B represents the curve of the battery cell at a charging rate of C / 5. In an embodiment, at least one of the discharge rates can be at a rate typical for an electric vehicle, which is in the range of 1C to C / 5.

[0132] At step 620, the method continues by determining an optimized set of health metrics based on the first set of anode electrode potentials and the first set of cathode electrode potentials and the second set of anode electrode potentials and the second set of cathode electrode potentials. As discussed, regarding Figure 3C , the EP curve 300C can be determined by using the curves of the anode and cathode EPs versus the state of charge (SOC) at two different C-rates as inputs. In the examples of EP curve 300A and EP curve 300B, those rates are 5 and 100.

[0133] The EP curve 300C can be derived from the health metrics labeled θ = {a, b, c, d, e, f}, where and where the C-rate is defined as {j, k}, for example, in this example j = 5 and k = 100. Additionally, the health metrics θ = {a, b, c, d, e, f} can be estimated through the voltage and current of the vehicle battery cell. This can be achieved in two ways. The first method can be to use the battery cell voltage and current values {V k , I} during constant current (CC) charging or discharging. The second method can be to use the estimated open circuit voltage OCV k and the associated current I k when discharging the battery cell.

[0134] In the case of constant current charging or discharging, the optimization problem for deriving θ can be expressed as

[0135] In the case of having the estimated open circuit voltage OCV k and the associated current I k when discharging the battery cell, the optimization problem for deriving θ can be expressed as where Q k = Q k-1 + I k *Δt; Q0 = SOC0 * C N , where C N represents the nominal capacity of the original vehicle battery cell.

[0136] At step 625, the method continues by testing the vehicle battery based on the optimized health metrics after a first number of cycles. As discussed in FIG. 5, the results of testing the vehicle battery after 2 cycles, 50 cycles, 100 cycles, and 200 cycles are shown. At step 630, the method continues by determining one or more faulty battery cells of the vehicle battery based on the test of the vehicle battery. As Figure 5A , Figure 5B , Figure 5C and Figure 5D shown, a set of battery cells is identified as severely damaged. In addition, as the number of cycles increases, the damaged battery cells show faster degradation compared to other battery cells.

[0137] Method 600 may then end.

[0138] The description and summary sections may set forth one or more embodiments of the present disclosure conceived by the inventor(s), and thus, are not intended to limit the present disclosure and the appended claims.

[0139] Embodiments of the present disclosure have been described above by means of functional building blocks that illustrate the implementation of specified functions and their relationships. For ease of description, the boundaries of these functional building blocks have been arbitrarily defined herein. Alternative boundaries may be defined as long as the specified functions and their relationships can be appropriately performed.

[0140] The foregoing description of the specific embodiments will so fully reveal the general nature of the present disclosure that others can, by applying the knowledge of those skilled in the art, without departing from the general concept of the present disclosure, readily modify and / or adapt such specific embodiments for various applications without undue experimentation. Therefore, based on the teachings and guidance presented herein, such adaptations and modifications are intended to be within the meaning and scope of the equivalents of the disclosed embodiments. It should be understood that the language or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of this specification will be interpreted by those skilled in the art in light of the teachings and guidance.

[0141] The breadth and scope of the present disclosure should not be limited by the above exemplary embodiments.

[0142] Exemplary embodiments of the present disclosure have been presented. The present disclosure is not limited to these examples. These examples are presented herein for illustrative rather than limiting purposes. Based on the teachings contained herein, alternatives (including equivalents, extensions, variations, deviations, etc. of those described herein) will be apparent to those skilled in the relevant art(s). Such alternatives fall within the scope and spirit of the present disclosure.

Claims

1. A method for diagnosing the health of a vehicle battery using electrode potential estimation, comprising: For an original vehicle battery cell, determining a first set of anode electrode potentials and a first set of cathode electrode potentials of the original vehicle battery cell at a first charging rate (first C-rate); For the original vehicle battery cell, determining a second set of anode electrode potentials and a second set of cathode electrode potentials of the original vehicle battery cell at a second charging rate (second C-rate); Determining a set of optimized health indicators based on the first set of anode electrode potentials and the first set of cathode electrode potentials and the second set of anode electrode potentials and the second set of cathode electrode potentials; After a first number of cycles, testing the vehicle battery based on the optimized health indicators; And Determining one or more faulty battery cells of the vehicle battery based on the test of the vehicle battery.

2. The method according to claim 1, wherein the original vehicle battery cell is of a lithium-ion type.

3. The method according to claim 1, wherein the second charging rate (second C-rate) is greater than C / 100, where C represents the capacity of the vehicle battery measured in ampere-hours.

4. The method according to claim 1, further comprising identifying an appropriate range of the health indicators.

5. The method according to claim 1, wherein determining the set of optimized health indicators includes measuring a plurality of cell voltages and a plurality of current values during constant current charging or discharging of the original vehicle battery cell.

6. The method according to claim 5, wherein determining the set of optimized health indicators is based on solving an optimization problem, the optimization problem comprising: Where Q k = I * k * Δt, and Δt is the sampling period.

7. The method according to claim 5, wherein determining the set of optimized health indicators is based on: where a, b, c, d, e and f represent health indicator values.

8. The method according to claim 1, wherein determining the set of optimized health indicators includes measuring a plurality of cell open circuit voltages (OCVs) and a plurality of current values at a constant discharge rate of the original vehicle battery cell.

9. The method according to claim 7, wherein determining the set of optimized health indicators is based on solving an optimization problem, the optimization problem comprising: where Q k = Q k-1 + I k * Δt; Q0 = SOC0 * C N , where C N represents the nominal capacity of the original vehicle battery unit.

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